K-近邻算法之Python实现

1.

from numpy import *
import operator


class knn1:
    def createDataset(self):
        group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
        labels = ['A','A','B','B']
        return group,labels


    def KnnClassify(self,testX,trainX,labels,K):
        [N,M]=trainX.shape
    
    
        difference = tile(testX,(N,1)) - trainX 
        difference = difference ** 2 
        distance = difference.sum(1) 
        distance = distance ** 0.5
        sortdiffidx = distance.argsort()
    
 
        vote = {} 
        for i in range(K):
            ith_label = labels[sortdiffidx[i]];
            vote[ith_label] = vote.get(ith_label,0)+1 
        sortedvote = sorted(vote.items(),key = lam